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Ultrasensitive Label-Free Electrochemical Detection of Pseudomonas aeruginosa Using a Surface Molecularly Imprinted Polymer-Modified Screen-Printed Electrode
(2026-06-01)
Vongmanee, Naphatsawan
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Nampeng, Jindapa
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Pintavirooj, Chuchart
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Visitsattapongse, Sarinporn
Pseudomonas aeruginosa is a major opportunistic pathogen frequently associated with nosocomial infections, such as pneumonia, urinary tract infections, and wound infections, particularly in immunocompromised or hospitalized patients. These infections are often difficult to treat due to the pathogen’s intrinsic antibiotic resistance and biofilm-forming ability. Therefore, rapid and selective detection of P. aeruginosa is essential for early diagnosis and effective infection control. In this study, a novel surface-imprinted MIP design uniquely combines methacrylamide (MAM), acrylamide (AAM), and vinylpyrrolidone (VP) monomers to generate recognition cavities that are complementary to the surface morphology and physicochemical properties of Pseudomonas aeruginosa cells. Unlike traditional MIP approaches, this surface imprinting strategy provides improved stability and reproducibility, without relying on biological recognition elements like antibodies or aptamers. This novel approach enabled us to achieve an ultralow LOD of 1 CFU/mL over a linear range of 1–104 CFU/mL, demonstrating excellent analytical performance. In addition, the sensor exhibited good reproducibility with an RSD of 5–12%. The novelty of this work lies in the use of a surface-imprinted MIP strategy combined with a multi-monomer system to enhance bacterial recognition and sensing performance. Overall, the proposed MIP-based electrochemical biomimetic sensor offers a rapid, cost-effective, and portable platform with strong potential for the detection of P. aeruginosa in clinical and environmental applications.
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C. albicans Detection with Electrochemical Sensors by Using Molecular Imprinted Polymer Technique
(2026-03-01)
Vongmanee, Naphatsawan
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Nampeng, Jindapa
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Pintavirooj, Chuchart
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Visitsattapongse, Sarinporn
Candida albicans (C. albicans) is an opportunistic fungal pathogen and a major cause of nosocomial infections, especially in immunocompromised patients. Conventional diagnostic approaches such as blood culture and biochemical assays are accurate but require multi-step sample processing and prolonged turnaround times, limiting their applicability for rapid clinical screening. In the present study, we developed an electrochemical biosensor based on molecularly imprinted polymer (MIP) technology for the rapid and selective detection of intact C. albicans cells. The MIP layer was electropolymerized onto a screen-printed carbon electrode (SPCE), forming selective recognition cavities complementary to the fungal morphology. Electrochemical characterization and detection were performed using cyclic voltammetry in phosphate-buffered saline (PBS). The system demonstrated a wide linear detection range, enabling reliable quantification of C. albicans across concentrations spanning from 1 to 104 CFU/mL and achieved an ultralow limit of detection (LOD) of 1.30 CFU/mL, demonstrating high sensitivity. High selectivity was confirmed against E. coli, S. aureus, and P. aeruginosa, demonstrating that the imprinted cavities effectively distinguish fungal cells from bacterial contaminants. These findings highlight the promise of MIP-based electrochemical biosensors as a simple, low-cost, and portable alternative for early fungal diagnostics.
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Molecularly Imprinted Polymer-Based Electrochemical BioSensors for Haemophilus influenzae Rapid Detection
(2026-03-01)
Vongmanee, Naphatsawan
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Nampeng, Jindapa
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Pintavirooj, Chuchart
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Visitsattapongse, Sarinporn
Haemophilus influenzae (H. influenzae) is an important respiratory pathogen that can cause various invasive and non-invasive bacterial infections requiring rapid and sensitive detection. In recent years, electrochemical biosensors have emerged as a practical alternative for pathogen detection due to their high sensitivity, portability and short analysis time. Molecularly imprinted polymers (MIPs) are a class of synthetic receptors designed to mimic biological recognition through template-directed polymerization. In this study, an electrochemical biosensor based on MIPs was developed for the selective detection of H. influenzae. The polymeric film composed of methacrylamide (MAM), acrylamide (AAM), and vinylpyrrolidone (VP) monomers was fabricated on a gold screen-printed electrode (gold-SPE). The results of cyclic voltammetry (CV) revealed a strong redox current shift corresponding to bacteria concentrations within an analytical range of 1–10,000 CFU/mL with LOD 1.03 CFU/mL, with relative standard deviation (RSD) values below 9% across the tested concentration range. The optimized composition yielded and exhibited excellent selectivity when tested against non-target bacteria such as Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus.
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Bacterial Colony Counting and Classification System Based on Deep Learning Model
(2026-02-01)
Pintavirooj, Chuchart
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Bunkum, Manao
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Vongmanee, Naphatsawan
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Nampeng, Jindapa
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Visitsattapongse, Sarinporn
Microbiological analysis is crucial for identifying species, assessing infections, and diagnosing infectious diseases, thereby supporting both research studies and medical diagnosis. In response to these needs, accurate and efficient identification of bacterial colonies is essential. Conventionally, this process is performed through manual counting and visual inspection of colonies on agar plates. However, this approach is prone to several limitations arising from human error and external factors such as lighting conditions, surface reflections, and image resolution. To overcome these limitations, an automated bacterial colony counting and classification system was developed by integrating a custom-designed imaging device with advanced deep learning models. The imaging device incorporates controlled illumination, matte-coated surfaces, and a high-resolution camera to minimize reflections and external noise, thereby ensuring consistent and reliable image acquisition. Image-processing algorithms implemented in MATLAB were employed to detect bacterial colonies, remove background artifacts, and generate cropped colony images for subsequent classification. A dataset comprising nine bacterial species was compiled and systematically evaluated using five deep learning architectures: ResNet-18, ResNet-50, Inception V3, GoogLeNet, and the state-of-the-art EfficientNet-B0. Experimental results demonstrated high colony-counting accuracy, with a mean accuracy of 90.79% ± 5.25% compared to manual counting. The coefficient of determination (R2 = 0.9083) indicated a strong correlation between automated and manual counting results. For colony classification, EfficientNet-B0 achieved the best performance, with an accuracy of 99.78% and a macro-F1 score of 0.99, demonstrating strong capability in distinguishing morphologically distinct colonies such as Serratia marcescens. Compared with previous studies, this research provides a time-efficient and scalable solution that balances high accuracy with computational efficiency. Overall, the findings highlight the potential of combining optimized imaging systems with modern lightweight deep learning models to advance microbiological diagnostics and improve routine laboratory workflows.
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Electrochemical Characterization of a Molecularly Imprinted Polymer Sensor for the Selective Recognition of Type II Collagen in Joint Degeneration Monitoring
(2026-02-01)
Nampeng, Jindapa
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Vongmanee, Naphatsawan
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Pintavirooj, Chuchart
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Visitsattapongse, Sarinporn
Type II collagen is a primary fibrillar component of articular cartilage, and its early degradation is a key biomarker of joint-degenerative disorders such as osteoarthritis, rheumatoid arthritis, gout, etc. Reliable detection at low concentrations remains challenging due to limited assay accessibility, complex analytical procedures, and nonspecific responses in multicomponent biological matrices. This research reports the development of a Molecularly Imprinted Polymer (MIP)–based electrochemical sensor engineered for the selective recognition of type II collagen. A series of monomer formulations were evaluated, and the 1AAM:2VP composition produced a well-defined imprinted layer on screen-printed carbon electrodes, yielding the highest electrochemical sensitivity and linearity. The optimized sensor exhibited strong anodic and cathodic responses proportional to increasing collagen concentrations, with a calibration slope corresponding to an R2 value of 0.9394. Minimal signal interference was observed, confirming high molecular selectivity. The limit of detection (LOD) was calculated to be approximately 0.065 µg/mL. These characteristics demonstrate that the proposed MIP sensor provides a low-cost, accessible, and highly selective analytical platform suitable for early-stage cartilage degeneration monitoring.
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Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images
(2026-01-01)
Visitsattapongse, Sarinporn
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Rithcharung, Preeyarat
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Santiprabhob, Jeerunda
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Lertbannaphong, Ornsuda
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Sermsripong, Wasawat
Background: Diabetes mellitus (DM) is a chronic metabolic disease characterized by dysregulated blood glucose. Prolonged DM can lead to diabetic retinopathy (DR), in which retinal capillaries are damaged by sustained hyperglycemia. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for visualizing retinal microvasculature and can detect early changes in both DM patients with and without DR. However, it requires expert evaluation, making early detection costly and time-consuming. This study aimed to develop a high-performance deep learning framework that can classify OCTA images into three groups of DM, such as normal, good glycemic control, and poor glycemic control. Methods: OCTA datasets of horizontal B-scans and en face scans from 300 participants aged 8–18 years were analyzed, including normal controls, DM patients with good glycemic control, and DM patients with poor control (HbA1c ≥8%). For each participant, a 3 mm × 3 mm foveal-centered en face image of the deep capillary plexus (DCP) and a horizontal B-scan through the foveal center of the right eye were selected. Several convolutional and transformer-based models were evaluated, with ConvNeXt (a ConvNet for the 2020s) chosen as the baseline for its superior performance. To enhance generalization and convergence, progressive resizing and the Lookahead optimization strategy were applied, while class-wise augmentation was used to balance the training set without altering the test distribution. Results: The baseline ConvNeXt achieved F1 scores of 0.7877 (B-scans) and 0.7424 (en face). After doing enhancement using progressive resizing and Lookahead optimization, performance improved to 0.8319 and 0.8567 (Wilcoxon signed-rank tests, P<0.05). Conclusions: Our proposed method for DM classification from OCTA images provided promising results while ensuring resource efficiency and rapid evaluation. Clinically, accurate classification of DM status is valuable for assessing the risk of DR progression. Thus, it can be served as an assistive tool for clinical decision support in DR management.
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The brain activation on upper extremity motor control tasks in different forces levels
(2025-12-01)
Kulwattho, Nattapat
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Chieh, Hsiao Feng
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Lin, Chien Ju
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Chen, Wei Jen
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Pintavirooj, Chuchart
Motor control is a critical process for muscle contraction initiated by nerve impulses governed by the motor cortex, which is vital for performing activities of daily living. The purpose of this study is to investigate brain activation in upper extremity motor control tasks in regulating the pushing force. Eighteen healthy young adults were asked to perform upper extremity motor control tasks, and recorded the hemodynamic signals using Functional Near-Infrared Spectroscopy and robotic arms. Two types of movement—static and dynamic—and three different task difficulties based on different force levels were used as force-regulating upper extremity motor control tasks. The hemodynamic response was collected in the primary motor cortex (M1), premotor cortex (PMC), supplementary motor area (SMA), and prefrontal cortex (PFC). The results showed a decrease in HbO for PFC was greater in the static relative to dynamic movement. Moreover, contralateral (c) M1, ipsilateral (i) PFC, and PMC have a significant increment in HbO mean compared to task difficulty. These findings indicate that the upper-extremity force and movement rely on separate cortical circuits: brain activation increases with difficulty in the cM1, PMC, and iPFC, whereas only the PFC distinguishes static from dynamic movement.
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Biosensor Based on Electrochemical Analysis for Staphylococcus aureus Detection with Molecular Imprinted Polymer Technique
(2025-11-01)
Vongmanee, Naphatsawan
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Nampeng, Jindapa
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Pintavirooj, Chuchart
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Visitsattapongse, Sarinporn
Staphylococcus aureus (S. aureus) is one of the most common hospital-acquired pathogens and poses a serious threat to patients with weakened immune systems. Transmission can occur through foodborne illness, skin infections, abscess formation, and bloodstream invasion. The most severe complication arises when S. aureus infects the heart, leading to valve damage and potentially progressing to heart failure. In addition, many strains have developed strong resistance to conventional antibiotic therapies, making treatment increasingly difficult. These challenges highlight the importance of early detection for effective prevention and management. This research focuses on the development of a polymer composite incorporating hydroxyproline for the preparation of molecularly imprinted polymers (MIPs) designed for the rapid detection of S. aureus. The sensing platform, based on electrochemical principles, enabled sensitive and efficient analysis of bacterial samples. The sensor exhibited a broad analytical range, detecting S. aureus from 1 to 10,000 CFU/mL, with a detection limit as low as 1.031 CFU/mL. Selectivity testing against Pseudomonas aeruginosa, Candida albicans, and Escherichia coli confirmed high specificity toward S. aureus. These findings highlight the potential of this MIP-based electrochemical sensor as a reliable tool for rapid bacterial detection in clinical and environmental settings.
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A Novel Approach for Optimizing Molecularly Imprinted Polymer Composition in Electrochemical Detection of Collagen Peptides
(2025-11-01)
Vongmanee, Naphatsawan
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Nampeng, Jindapa
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Rattanapithan, Katesirin
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Sriwichai, Phuritasinee
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Pintavirooj, Chuchart
Collagen peptides are key structural proteins that play an important role in maintaining the integrity and proper function of multiple tissues in the human body. Their breakdown is recognized as an important biomarker for various degenerative conditions, including the loss of muscle mass, joint and bone disorders, and compromised skin health. Current analytical approaches for collagen detection, such as ultraviolet spectrometry, enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and histochemical staining, are widely used but often expensive, time-consuming, and reliant on specific laboratory instrumentation, limiting their practicality for routine or rapid diagnostics. This study reports a novel biosensor for collagen peptide detection based on molecularly imprinted polymers (MIPs) integrated with screen-printed electrodes (SPEs). Electrochemical measurements revealed a clear correlation between collagen concentration and current response, confirming effective molecular binding within the imprinted matrix. The optimized MIP-modified electrode exhibited a detection range of 0.1–1000 µg/mL with a limit of detection (LOD) of 1.0106 µg/mL, limit of quantification (LOQ) of 4.46 µg/mL, sensitivity of 8.3816, and correlation coefficient (R2 = 0.9436). These results highlight strong selectivity and sensitivity toward collagen peptides. The proposed MIP-based biosensor provides a rapid, low-cost platform for detecting collagen degradation products and holds potential for early diagnosis and future clinical applications in degenerative disease monitoring.
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Electrochemical Biosensors by Means of Molecularly Imprinted Polymers (MIPs) Cortisol Recognition
(2025-02-01)
Nampeng, Jindapa
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Vongmanee, Naphatsawan
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Pintavirooj, Chuchart
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Chiu, Wen Tai
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Visitsattapongse, Sarinporn
Depression and anxiety are two common mental health issues that require serious attention, as they have significant impacts on human well-being, with both being emotionally and physically reflected in the increasing number of suicide cases globally. The World Health Organization (WHO) estimated that about 322 million people around the world experienced mental illnesses in 2017, and this number continues to increase. Cortisol is a major stress-controlled hormone that is regulated by the hypothalamic–pituitary–adrenal (HPA) axis. The HPA axis has three main components, including the hypothalamus, pituitary gland, and adrenal gland, where cortisol, the primary stress hormone, is released. It plays crucial roles in responding to stress, energy balance, and the immune system. The cortisol level in the bloodstream usually increases when stress develops. Molecularly imprinted polymers (MIPs) have been highlighted in terms of creating artificial bioreceptors by mimicking the shape of detected biomolecules, making natural bioreceptor molecules no longer required. MIPs can overcome the limitations of chemicals and physical properties reducing over time and the short-time shelf life of natural bioreceptors. MIPs’ benefits are reflected in their ease of use, high sensitivity, high specificity, reusability, durability, and the lack of requirement for complicated sample preparation before use. Moreover, MIPs incur low costs in manufacturing, giving them a favorable budget for the market with simple utilization. MIPs can be formulated by only three key steps, including formation, the polymerization of functional monomers, and the creation of three-dimensional cavities mimicking the shape and size of targeting molecules. MIPs have a high potential as biosensors, especially working as bioanalytics for protein, anti-body, antigen, or bacteria detection. Herein, this research proposes an MIP-based cortisol biosensor in which cortisol is imprinted on methyl methacrylate (MMA) and methacrylic acid (MAA) produced by UV polymerization. This MIP-based biosensor may be an alternative method with which to detect and monitor the levels of hormones in biological samples such as serum, saliva, or urine due to its rapid detection ability, which would be of benefit for diagnosing depression and anxiety and prescribing treatment. In this study, quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of a cortisol solution ranging from 0.1 to 1000 pg/mL. The MIP-based biosensor, as derived by calculation, achieved its best detection limit of 1.035 pg/mL with a gold electrode. Tests were also performed on molecules with a similar molecular structure, including Medroxyprogesterone acetate and drospirenone, to ensure the sensitivity and accuracy of the sensors, demonstrating a low sensitivity and low linear response.

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